Senior MLOps Engineer x 2

22 hours ago

Sydney, New South Wales, Australia AI Talent Full time $65,000 - $70,000 per year

We are seeking a highly skilled
Senior MLOps Engineer
to join our client, a leader in the
Gambling, Wagering, and Entertainment
sector. This is a crucial role focused on transforming experimental ML models into reliable, secure, and continuously running production services that drive customer experience and business
optimisation
.

This initial
6-month contract
, with a high likelihood of a further
6-month extension
, requires an expert capable of delivering immediate impact. The successful candidate will be offered a competitive rate of
$650 - $700 per day
(inclusive of superannuation/on-costs). For the right candidate with the necessary skills and experience, we are pleased to offer
482 visa sponsorship
.

What You'll Do

  • Design, build, and govern
    the end-to-end MLOps platform and CI/CD pipelines for deploying, testing, and managing production machine learning models at scale.
  • Operationalise
    complex models (e.g., recommendation engines, risk prediction, customer lifetime value) developed in Python (using libraries like
    PyTorch and TensorFlow
    ).
  • Develop and manage feature stores
    and real-time inference services to ensure low-latency model predictions are available for consumer-facing applications.
  • Collaborate closely with Data Scientists
    to implement coding standards, version control, and reproducible experimentation across all models.
  • Implement robust monitoring, alerting, and drift detection
    for models in production, ensuring model fairness, security, and adherence to Responsible AI principles.
  • Architect data processing pipelines
    using distributed computing frameworks (like
    Spark
    ) to prepare large-scale datasets for model training and retraining.
  • Advise on and implement best practises
    in machine learning algorithms, experimental design, statistical modelling, and model interpretability across the data science team.
  • Translate complex ML solutions
    and technical findings into clear business impact and present results to both technical and non-technical stakeholders.

Key Skills and Experience

  • Expert proficiency in
    Python
    and standard ML libraries (
    scikit-learn, PyTorch, TensorFlow
    ) for model development and deployment.
  • Extensive hands-on experience with modern ML and cloud platforms (e.g.,
    Databricks, SageMaker, Azure ML, or Vertex AI
    ).
  • Deep practical knowledge of
    MLOps practices
    including model versioning, automated deployment, monitoring, and continuous retraining.
  • Familiarity with large-scale data processing and distributed computing frameworks (e.g.,
    Spark, Ray, Dask
    ).
  • Strong understanding of feature engineering, model interpretability, fairness, and
    Responsible AI principles
    .
  • Solid background in statistical modelling and experimental design.
  • Experience
    architecting
    systems on a major public cloud platform (AWS, Azure, or GCP).

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